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Proceedings Paper

Three-dimensional correlation filters for orientation invariant recognition
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Paper Abstract

Correlation filters are ideally suited for recognizing patterns in 3D data. Whereas most model-based techniques tend to measure the overall dimensions of objects and their larger features, correlation filters can readily exploit intricate surface details, the gray values of surfaces as well as internal structure, if any. Thus correlation filters may be the preferred approach in scenarios when intensity and range data are both available, or when the internal structure of an object has been mapped. In this paper, we outline the development of filters for 3D data that we refer to as Volume Correlation Filters, illustrate their use with range images of an object, and outline future work for the development of 3D correlation techniques.

Paper Details

Date Published: 22 October 2001
PDF: 8 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445396
Show Author Affiliations
Abhijit Mahalanobis, Lockheed Martin Corp. (United States)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)
Alan J. Van Nevel, Naval Air Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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